import json
from abc import ABC, abstractmethod
from typing import Type

import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import tool


class Person(BaseModel):
    name: str = Field(..., description="The name of the person.")
    age: int = Field(..., description="The age of the person.")


@tool
def my_adder_tool(a: int, b: int) -> int:
    """Takes two integers, a and b, and returns their sum."""
    return a + b


class ChatModelIntegrationTests(ABC):
    @abstractmethod
    @pytest.fixture
    def chat_model_class(self) -> Type[BaseChatModel]:
        ...

    @pytest.fixture
    def chat_model_params(self) -> dict:
        return {}

    @pytest.fixture
    def chat_model_has_tool_calling(
        self, chat_model_class: Type[BaseChatModel]
    ) -> bool:
        return chat_model_class.bind_tools is not BaseChatModel.bind_tools

    @pytest.fixture
    def chat_model_has_structured_output(
        self, chat_model_class: Type[BaseChatModel]
    ) -> bool:
        return (
            chat_model_class.with_structured_output
            is not BaseChatModel.with_structured_output
        )

    def test_invoke(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        result = model.invoke("Hello")
        assert result is not None
        assert isinstance(result, AIMessage)
        assert isinstance(result.content, str)
        assert len(result.content) > 0

    async def test_ainvoke(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        result = await model.ainvoke("Hello")
        assert result is not None
        assert isinstance(result, AIMessage)
        assert isinstance(result.content, str)
        assert len(result.content) > 0

    def test_stream(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        num_tokens = 0
        for token in model.stream("Hello"):
            assert token is not None
            assert isinstance(token, AIMessageChunk)
            assert isinstance(token.content, str)
            num_tokens += len(token.content)
        assert num_tokens > 0

    async def test_astream(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        num_tokens = 0
        async for token in model.astream("Hello"):
            assert token is not None
            assert isinstance(token, AIMessageChunk)
            assert isinstance(token.content, str)
            num_tokens += len(token.content)
        assert num_tokens > 0

    def test_batch(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        batch_results = model.batch(["Hello", "Hey"])
        assert batch_results is not None
        assert isinstance(batch_results, list)
        assert len(batch_results) == 2
        for result in batch_results:
            assert result is not None
            assert isinstance(result, AIMessage)
            assert isinstance(result.content, str)
            assert len(result.content) > 0

    async def test_abatch(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        batch_results = await model.abatch(["Hello", "Hey"])
        assert batch_results is not None
        assert isinstance(batch_results, list)
        assert len(batch_results) == 2
        for result in batch_results:
            assert result is not None
            assert isinstance(result, AIMessage)
            assert isinstance(result.content, str)
            assert len(result.content) > 0

    def test_conversation(
        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
    ) -> None:
        model = chat_model_class(**chat_model_params)
        messages = [
            HumanMessage(content="hello"),
            AIMessage(content="hello"),
            HumanMessage(content="how are you"),
        ]
        result = model.invoke(messages)
        assert result is not None
        assert isinstance(result, AIMessage)
        assert isinstance(result.content, str)
        assert len(result.content) > 0

    def test_tool_message_histories_string_content(
        self,
        chat_model_class: Type[BaseChatModel],
        chat_model_params: dict,
        chat_model_has_tool_calling: bool,
    ) -> None:
        """
        Test that message histories are compatible with string tool contents
        (e.g. OpenAI).
        """
        if not chat_model_has_tool_calling:
            pytest.skip("Test requires tool calling.")
        model = chat_model_class(**chat_model_params)
        model_with_tools = model.bind_tools([my_adder_tool])
        function_name = "my_adder_tool"
        function_args = {"a": "1", "b": "2"}

        messages_string_content = [
            HumanMessage(content="What is 1 + 2"),
            # string content (e.g. OpenAI)
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "name": function_name,
                        "args": function_args,
                        "id": "abc123",
                    },
                ],
            ),
            ToolMessage(
                name=function_name,
                content=json.dumps({"result": 3}),
                tool_call_id="abc123",
            ),
        ]
        result_string_content = model_with_tools.invoke(messages_string_content)
        assert isinstance(result_string_content, AIMessage)

    def test_tool_message_histories_list_content(
        self,
        chat_model_class: Type[BaseChatModel],
        chat_model_params: dict,
        chat_model_has_tool_calling: bool,
    ) -> None:
        """
        Test that message histories are compatible with list tool contents
        (e.g. Anthropic).
        """
        if not chat_model_has_tool_calling:
            pytest.skip("Test requires tool calling.")
        model = chat_model_class(**chat_model_params)
        model_with_tools = model.bind_tools([my_adder_tool])
        function_name = "my_adder_tool"
        function_args = {"a": 1, "b": 2}

        messages_list_content = [
            HumanMessage(content="What is 1 + 2"),
            # List content (e.g., Anthropic)
            AIMessage(
                content=[
                    {"type": "text", "text": "some text"},
                    {
                        "type": "tool_use",
                        "id": "abc123",
                        "name": function_name,
                        "input": function_args,
                    },
                ],
                tool_calls=[
                    {
                        "name": function_name,
                        "args": function_args,
                        "id": "abc123",
                    },
                ],
            ),
            ToolMessage(
                name=function_name,
                content=json.dumps({"result": 3}),
                tool_call_id="abc123",
            ),
        ]
        result_list_content = model_with_tools.invoke(messages_list_content)
        assert isinstance(result_list_content, AIMessage)

    def test_structured_few_shot_examples(
        self,
        chat_model_class: Type[BaseChatModel],
        chat_model_params: dict,
        chat_model_has_tool_calling: bool,
    ) -> None:
        """
        Test that model can process few-shot examples with tool calls.
        """
        if not chat_model_has_tool_calling:
            pytest.skip("Test requires tool calling.")
        model = chat_model_class(**chat_model_params)
        model_with_tools = model.bind_tools([my_adder_tool])
        function_name = "my_adder_tool"
        function_args = {"a": 1, "b": 2}
        function_result = json.dumps({"result": 3})

        messages_string_content = [
            HumanMessage(content="What is 1 + 2"),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "name": function_name,
                        "args": function_args,
                        "id": "abc123",
                    },
                ],
            ),
            ToolMessage(
                name=function_name,
                content=function_result,
                tool_call_id="abc123",
            ),
            AIMessage(content=function_result),
            HumanMessage(content="What is 3 + 4"),
        ]
        result_string_content = model_with_tools.invoke(messages_string_content)
        assert isinstance(result_string_content, AIMessage)